Remove spurious peaks of Hilbert transform for LP residual

In summary, the conversation discusses using a 4-step process for eliminating spurious peaks in the Hilbert transform, specifically for finding Glottal closure in LP residual. The steps involved are down-sampling, performing the Hilbert transform, identifying peaks in the transform, and considering a hypothesis about the time gap between two successive glottal closure instances. A MATLAB code is also provided for reference.

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  • #1
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Homework Statement


I want to eliminate spurious peaks of Hilbert transform for finding Glottal closure in LP residual. I have 4 step :

Homework Equations


1-down-sample.

2-Hilbert Transform.

3-Identify Peaks in Hilbert Transform.

4-consider this hypothesis that time gap between two successive glottal closure instance is not likely to vary in the adjacent pitch period.

I have wrote following MATLAB code:

The Attempt at a Solution



e_downsample = downsample(e,2);
DFT=fft(e);
hilbert_e=ifft(complex(imag(DFT(1:(length(DFT)/2))),real(DFT(1:(length(DFT)/2)))));
h_e=sqrt(e_downsample(:).^2-hilbert_e(:).^2);
[pks,locs] = findpeaks(abs(h_e));
figure,
plot(abs(h_e));

 

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  • #2
hold on;plot(locs,pks,'ro');for i=1:length(locs)-1 gap_period(i)= locs(i+1)-locs(i);endmean_gap_period=mean(gap_period);[pks,locs] = findpeaks(abs(h_e));for i=1:length(locs)-1 if (abs(locs(i+1)-locs(i))>(mean_gap_period*1.5)) locs(i+1)=0; endendlocs=locs(locs ~= 0); figure,plot(abs(h_e));hold on;plot(locs,pks(locs),'ro');
 

1. What is the purpose of removing spurious peaks in a Hilbert transform for LP residual?

The purpose of removing spurious peaks in a Hilbert transform for LP residual is to improve the accuracy and reliability of the LP residual signal. Spurious peaks can distort the LP residual signal and affect the results of subsequent analysis or processing.

2. How do you identify spurious peaks in a Hilbert transform for LP residual?

Spurious peaks can be identified by examining the magnitude of the Hilbert transform. If there are peaks that are significantly higher than the surrounding values, they are likely to be spurious and should be removed.

3. What causes spurious peaks in a Hilbert transform for LP residual?

Spurious peaks can be caused by noise, signal artifacts, or other sources of interference in the LP residual signal. They can also be a result of errors or limitations in the Hilbert transform algorithm.

4. How do you remove spurious peaks in a Hilbert transform for LP residual?

Spurious peaks can be removed by applying a smoothing or filtering technique to the LP residual signal. This can help to reduce the impact of noise or artifacts and remove any erroneous peaks. Alternatively, the Hilbert transform algorithm can be modified to reduce the occurrence of spurious peaks.

5. How does removing spurious peaks affect the LP residual signal?

Removing spurious peaks can improve the quality and accuracy of the LP residual signal. It can help to remove unwanted noise and artifacts, making the signal more reliable for further analysis or processing. However, it is important to carefully consider the impact of any smoothing or filtering techniques on the overall signal before applying them.

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